System and method for improving accuracy of baseline models

ABSTRACT

System, method and computer-readable medium for baseline modeling a product or process. A service database contains process data. A preprocessor processes the data into a predetermined format. A baseline modeling component builds a baseline model from the preprocessed data, wherein the baseline model relates process performance variables as a function of process operating conditions.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of co-pending U.S.patent application Ser. No. 09/682,314, filed on Aug. 17, 2001, theentirety of which is incorporated by reference herein.

BACKGROUND OF THE INVENTION

The present invention relates generally to systems and methods forimproving the quality and productivity of a product or process and moreparticularly to baseline modeling of a product or process.

Baseline modeling of a product or process generally provides anunderstanding of the performance of an ideal product or process overtime. An engine is one type of product or process that baseline modelingis suitable for use. Engine baseline modeling has a multitude of usesincluding, but not limited to, determining when an engine performs outof specification, predicting when an engine failure will occur,detecting abnormal conditions, determining the quality of an engine anddesigning new engines. Typically, engine baseline models are developedfrom data gathered from thermodynamic cycle analyses and simulation.First, models of ideal values are created and indexed by variables suchas altitude, temperature, power setting, and air speed. Once data fromthe normal operation of the engine is available, these models areupdated by analyzing data corresponding to a particular modelcharacteristic. An engineer then looks for data that are similar for thespecified engine variables (e.g., altitude, temperature, power setting,air speed), groups the similar data, averages them for each variable andperforms other operations as desired. The engineer then plots data foreach of the variables. The plots provide interrelationship informationbetween each of the engine variables, which the engineer uses to createtables of typical operation parameters the baseline model. These tablesof parameters are used as the basis of comparison for engine operation.Differences from the baseline model may indicate engine faults ordeterioration trends.

There are several problems associated with this type of engine baselinemodeling. First, this type of engine baseline modeling is very laborintensive because the engineer has to review the data, find data thatare similar, group and average the data, perform other desiredoperations on the data, plot the data and create tables. Another problemis that one engineer cannot readily reproduce an engine baseline modeldeveloped by another engineer because this process is veryindividualized. It is helpful if one engineer can reproduce the enginebaseline model generated by another engineer to validate quality of thebaseline. Another problem associated with this type of engine baselinemodel is that the resulting model does not provide a good picture of anengine operating outside normal conditions. Furthermore, this type ofengine baseline modeling does not provide a measure of how good thedeveloped model is.

Accordingly, there is a need in the art of statistical modeling for anautomated approach to engine baseline modeling that standardizes theprocess to improve reliability by minimizing human interventions.

BRIEF SUMMARY OF THE INVENTION

The present invention overcomes the problems noted above, and providesadditional advantages, by providing a system, method and computerreadable medium that stores instructions for instructing a computersystem, to perform engine baseline modeling. In one embodiment of thepresent invention, an engine service database contains engine data,wherein the engine data includes at least time-varying engine data. Apreprocessor for processing the engine data into a predetermined format.An engine baseline modeling component builds an initial engine baselinemodel from the preprocessed data using a regression analysis, whereinthe regression analysis relates engine performance variables as afunction of engine operating conditions. The engine baseline modelingcomponent then applies a smoothing algorithm to the initial enginebaseline model to reduce effects of the time-varying engine data andgenerate a detrended engine baseline model.

In a second aspect of this disclosure, there is a system, method andcomputer readable medium that stores instructions for instructing acomputer system, to perform engine baseline modeling. In thisembodiment, an engine service database contains engine data. Apreprocessor processes the engine data into a predetermined format,wherein the preprocessor comprises a data segmenting component thatsegments the engine data into a plurality of groups. An engine baselinemodeling component builds an initial engine baseline model from thepreprocessed data using a regression analysis, wherein the regressionanalysis relates engine performance variables as a function of engineoperating conditions. The engine baseline modeling component identifiescorrelated groups of engine data based upon the initial engine baselinemodel and combines data from correlated groups. The engine baselinemodeling component then builds a final engine baseline model from thecombined data using a regression analysis.

In a third aspect of this disclosure, there is a system, method andcomputer readable medium that stores instructions for instructing acomputer system, to perform engine baseline modeling of an aircraftengine. In this embodiment, an engine service database contains aircraftengine data. A preprocessor processes the engine data into apredetermined format, wherein the preprocessor comprises a datasegmenting component that segments the engine data into a plurality ofgroups. An engine baseline modeling component that builds an initialengine baseline model from the preprocessed data using a regressionanalysis, the initial engine baseline model represented by a pluralityof parameter estimates, wherein the regression analysis relates engineperformance variables as a function of engine operating conditions. Theengine baseline modeling component then identifies segments relating torelated engines and averages the parameter estimates for each of theidentified related engine segments. The engine baseline modelingcomponent then builds a final engine baseline model from the averageddata using a regression analysis.

BRIEF DESCRIPTION OF DRAWINGS

The present invention can be understood more completely by reading thefollowing Detailed Description of Preferred Embodiments, in conjunctionwith the accompanying drawings.

FIG. 1 shows a schematic diagram of a general-purpose computer system inwhich a system for performing engine baseline modeling operates.

FIG. 2 shows a top-level component architecture diagram of the enginebaseline modeling system that operates on the computer system shown inFIG. 1.

FIG. 3 shows a flow chart describing actions performed by the enginebaseline modeling system shown in FIG. 2.

FIG. 4 shows an architectural diagram of a system for implementing theengine baseline modeling system shown in FIG. 2.

FIG. 5 is a flow chart describing an alternative embodiment of actionsperformed by the engine baseline modeling system shown in FIG. 2.

FIG. 6 is a flow chart describing another alternative embodiment ofactions performed by the engine baseline modeling system shown in FIG.2.

FIG. 7 is a flow chart describing yet another alternative embodiment ofactions performed by the engine baseline modeling system shown in FIG.2.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

This disclosure describes a system, method and computer product forbaseline modeling of a product or process such as an aircraft engine,however, the disclosure is applicable to any type of product or processwhere it is desirable to model performance. FIG. 1 shows a schematicdiagram of a general-purpose computer system in which a system forperforming engine baseline modeling operates. The computer system 10generally comprises a processor 12, memory 14, input/output devices, anddata pathways (e.g., buses) 16 connecting the processor, memory andinput/output devices. The processor 12 accepts instructions and datafrom memory 14 and performs various operations. The processor 12includes an arithmetic logic unit (ALU) that performs arithmetic andlogical operations and a control unit that extracts instructions frommemory 14 and decodes and executes them, calling on the ALU whennecessary. The memory 14 generally includes a random-access memory (RAM)and a read-only memory (ROM), however, there may be other types ofmemory such as programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM) and electrically erasableprogrammable read-only memory (EEPROM). Also, memory 14 preferablycontains an operating system, which executes on the processor 12. Theoperating system performs basic tasks that include recognizing input,sending output to output devices, keeping track of files and directoriesand controlling various peripheral devices.

The input/output devices may comprise a keyboard 18 and a mouse 20 thatenter data and instructions into the computer system 10. Also, a display22 may be used to allow a user to see what the computer hasaccomplished. Other output devices may include a printer, plotter,synthesizer and speakers. A communication device 24 such as a telephoneor cable modem or a network card such as an Ethernet adapter, local areanetwork (LAN) adapter, integrated services digital network (ISDN)adapter, Digital Subscriber Line (DSL) adapter or wireless access card,enables the computer system 10 to access other computers and resourceson a network such as a LAN, wireless LAN or wide area network (WAN). Amass storage device 26 may be used to allow the computer system 10 topermanently retain large amounts of data. The mass storage device mayinclude all types of disk drives such as floppy disks, hard disks andoptical disks, as well as tape drives that can read and write data ontoa tape that could include digital audio tapes (DAT), digital lineartapes (DLT), or other magnetically coded media. The above-describedcomputer system 10 can take the form of a hand-held digital computer,personal digital assistant computer, notebook computer, personalcomputer, workstation, mini-computer, mainframe computer orsupercomputer.

FIG. 2 shows a top-level component architecture diagram of an enginebaseline modeling system 28 that operates on the computer system 10shown in FIG. 1. Generally, the engine baseline modeling system 28models the performance of an “ideal engine for a specified type aircraftengine. An engine baseline model built with the engine baseline modelingsystem 28 has a multitude of uses. An illustrative, but non-exhaustivelist of potential uses for an engine baseline model built from theengine baseline modeling system 28 includes monitoring engine status,predicting future engine behavior, diagnosing engine faults, determiningwhen an engine performs out of specification, determining the quality ofan engine and designing new systems for an engine.

In FIG. 2, there is an engine service database 30 that contains enginedata for a variety of aircraft engines. The engine data comprises anassortment of engine performance information for each of the engines.Generally, engine performance information includes environmental data inwhich the engines operate such as altitude, air temperature, air speed,engine loading, engine temperature and pressure. One of ordinary skillin the art will recognize that the engine service database 30 maycomprises other engine performance information such as mach, fan speed,etc. In addition, the engine service database 30 may comprise other datasuch as operational data. A non-exhaustive list of engine operationaldata stored in the engine service database 30 includes exhaust gastemperatures (EGT), shaft speed between compressors and turbines (Ni andN2), pressure combustion (Pa) and fuel flow (WF). One of ordinary skillin the art will recognize that other engine operational data may includeengine bleed settings, vibration readings, and control mechanismsettings. Also, the engine service database 30 may comprise other datasuch as aircraft operating and settings data (e.g., bleed settings).

One of ordinary skill in the art will recognize that there are a varietyof approaches to acquiring the above data and storing them in the engineservice database 30. For example, some data can be capturedautomatically using on-line data acquisition techniques, while otherdata can be captured using manually recording techniques or onboard datacapture techniques. Furthermore, the engine service database 30preferably stores the data in a format that permits users to import thedata into other tools for further analysis, such as Microsoft EXCEL®,Minitab, and SAS.

The engine service database 30 may comprise other types of data for theengines. For example, the engine service database 30 may compriseservice information for the engines. Generally, the service informationwill comprise information such as engine product information, ageinformation of the engines and repair history of the engines (e.g.,dates of service events, types of service events, etc.). Other types ofengine data stored in the engine service database 30 may includein-flight data, engine utilization data (e.g., where, when, how flown),ownership data, remote monitoring and diagnostics status data.

Referring to FIG. 2, the engine baseline modeling system 28 comprises apreprocessor 32 that processes the engine data into a predeterminedformat. In particular, the preprocessor 32 comprises a data acquisitioncomponent 38 that extracts the engine data from the engine servicedatabase 30. The data acquisition component 38 acquires the service databy using commercially available modules available from Minitab,Microsoft, Oracle, etc. which directly extract the data into the enginebaseline modeling system 28, however, one of ordinary skill in the artwill recognize that one can write specialized code to extract the datainto a common format and write additional specialized code to importthat into the system.

The preprocessor 32 performs computations that simplify futureprocessing of the data, while a data scrubbing component 40 cleans theengine data. In particular, the preprocessor 32 performs operations thatconvert the data into standard units. For example, the preprocessor 32can convert temperature data from Celsius to Kelvin or correct enginepower setting data by the engine bleed settings. Other types ofcorrections that the preprocessor 32 may perform include convertingpounds to kilograms, altitude to pressure, knots and altitude to machnumber. One of ordinary skill in the art will recognize that the listedcorrections are only illustrative of some possibilities and are notexhaustive of other possibilities. Furthermore, one of ordinary skill inthe art will recognize that the preprocessor 32 can perform thecorrections in any manner desired (e.g., Celsius to Rankine) and is notlimited to the above order. Examples of cleaning operations performed bythe data scrubbing component 40 include discarding data elements withmissing values, correcting simple typographical errors, discarding dataelements with erroneous values out of reasonable operating range, etc.One of ordinary skill in the art will recognize that the listed cleaningoperations are only illustrative of some possibilities and are notexhaustive of other possibilities.

In addition, the preprocessor also comprises a data segmenting component42 that segments engine data into groups, nodes or clusters thatrepresent similar operating conditions. The groups generally includeengine performance variables such as power setting, altitude, air speed(mach number), and air temperature. One of ordinary skill in the artwill recognize that other engine performance variables such as airhumidity and control settings may be selected and that the disclosureshould not be limited to these variables. Once the groups have beenselected, then the data segmenting component 42 can segment the datainto the particular group that it relates to. Once the data aresegmented into the groups, then the data segmenting component 42 can usea cluster analysis to determine clusters of operating conditions.Alternatively, an engineer may assign bands of operations of interestfor each of the variables.

An engine baseline modeling component 34 builds an engine baseline modelfrom the data processed by the preprocessor 32. In particular, theengine baseline model built by the engine baseline modeling componentrelates the selected performance variables as a function of engineoperating conditions using the processed data. Engine operatingconditions include engine, aircraft and environmental conditions. Inthis disclosure, the engine baseline model is built from a regressionanalysis. Generally speaking, a regression is the statistical science ofdetermining an equation from a finite number of points that providesvalues of Y for a given X, i.e., Y=f(X). In this disclosure, theequation to be determined can be expressed as:Y=f(altitude, temperature, power setting, air speed)  (1)

where altitude, temperature, power setting and air speed are the Xvariables. The engine baseline modeling component 34 performs aregression to determine the above equation for each of the selectedengine performance variables (i.e., power setting, altitude, air speed,and air temperature) during specified times that the engine isoperating. For instance, the engine modeling component 34 can performthe regression on the data taken during the take-off, climb and cruisefor any or all of the engine performance variables. One of ordinaryskill in the art will recognize that more engine performance variables(air humidity, control settings, etc.) or less engine performancevariables can be used in equation 1. In addition, one of ordinary skillin the art will recognize that different combinations of engineperformance variables can be used in equation 1.

In general, a regression fits a parametric equation to a set of data bysolving for values of regression parameters such that the best fit tothe data set is obtained. Multiple linear regression is a type ofregression that solves the system of equations, minimizing the combinederror. In this disclosure, the system of equations that the regressionsolves can be as follows:y[1]=a*power setting[1]+b*altitude[1]+c*temperature[1]+d*airspeed[1]+ .. . +error[1]y[2]=a*power setting[2]+b*altitude[2]+c*temperature[2]+d*airspeed[2]+ .. . +error[2]. . .y[n]=a*power setting[n]+b*altitude[n]+c*temperature[n]+d*airspeed[n]+ .. . +error[n]  (2)

wherein a, b, c, d are the regression parameters and power setting[l],altitude[l], temperature[l], airspeed[l], y[l] are observed events.Again, one of ordinary skill in the art will recognize that the systemof equations can differ depending on the selection of engine performancevariables.

The resulting parameter estimates for a, b, c and d are representativeof the new baseline model. Instead of using tables to develop thebaseline model as was done in the past, there is now a simple equationthat describes the baseline behavior of the engine, from which eitherthe tables may be generated, or the equation can be applied directly.For example, a baseline model for the exhaust gas temperature (EGT)parameter might be as follows:EGT=0.1*power+0.001*altitude+0.01*temperature+0.05*airspeed  (3)

In this example, an engine that had power set to 100, altitude at 1000,temperature at 300, and air speed at 200, would result in a predictedEGT value of 24 degrees, but might have a measured EGT value of 14degrees, which would indicate that the engine was 10 degrees below thepredicted value.

The engine baseline modeling component 34 also comprises a metriccomponent 44 that validates the engine baseline model. In particular,the metric component 44 validates the engine baseline model by examiningthe quality of the built model. In this disclosure, the metric component44 determines the goodness of model fit by analyzing statisticalgoodness of fit metrics, such as R-squared, which is a common regressiontool output. One of ordinary skill in the art will recognize that themetric component 44 can determine other metrics besides the R-squaredmetric such as the mean square error, sum square error and sigmametrics, which are other common regression tool outputs.

The engine baseline modeling component 34 also comprises a data cleaningheuristics component 46 that cleans the preprocessed data according to aset of heuristics. Generally, the data cleaning heuristics component 46uses heuristics to remove data that does not conform to the norm. Anillustrative, but non-exhaustive list of data that the data cleaningheuristics component 46 removes includes regression outliers, regressionleverage points, and faulty engines. In this disclosure, this datacleaning operation can be performed for groups of engines or fleets ofaircraft that use a common engine.

Additional details and embodiments relating to the engine baselinemodeling component 34 will be set forth in detail below. In particular,inventive techniques are provided which additionally enhance the qualityand reliability of the generated model.

FIG. 2 also shows that the engine baseline modeling system 28 comprisesa model diagnostics component 36 that evaluates the performance of theengine baseline model. In particular, the model diagnostics component 36generates statistical outputs that provide statistical information to auser of the engine baseline modeling system 28. An illustrative, butnon-exhaustive list of the statistical outputs that the modeldiagnostics component 36 generates includes variance, r², collinearity,probability plots, residual plots, standard error measurements,confidence limits on the engine baseline model, prediction limits, pureerror lack-of-fit test, data subsetting lack-of-fit test,multicolinearity metrics (variance inflation factors), autocorrelationof residuals (Durbin-Watson statistic), etc.

The algorithms performed by the components in the engine baselinemodeling system 28 (i.e., the preprocessor 32, engine baseline modelingcomponent 34 and model diagnostics component 36 can be programmed with acommercially available statistical package such as SAS, but otherlanguages such as C or Java may be used.

The engine baseline modeling system 28 is not limited to a softwareimplementation. For instance, the preprocessor 32, engine baselinemodeling component 34 and model diagnostics component 36 may take theform of hardware or firmware or combinations of software, hardware, andfirmware. In addition, the engine baseline modeling system 28 is notlimited to the preprocessor 32, engine baseline modeling component 34and model diagnostics component 36. One of ordinary skill in the artwill recognize that the engine baseline modeling system 28 may haveother components.

FIG. 3 shows a flow chart describing actions performed by the enginebaseline modeling system 28 shown in FIG. 2. At block 48, the dataacquisition component 38 extracts the engine data from the engineservice database 30. Next, at 50 the user of the engine baselinemodeling system 28 selects a particular engine model and engineperformance variables for that engine that he or she would like to model(e.g., power setting, altitude, air speed and air temperature). Thepreprocessor 32 converts data into a standardized format at 52 and thedata scrubbing component 40 cleans the engine data at 54. The datasegmenting component 42 then segments the engine data into groups suchas altitude, air speed and air temperature, fuel specific heat value,air humidity, control settings, etc. at 56.

After the data segmenting component 42 has segmented the engine datainto groups, the engine baseline modeling component 34 builds an enginebaseline model from the data processed by the preprocessor 32. Inparticular, the building of the engine baseline model begins with theengine baseline modeling component 34 performing a regression todetermine parameters for each of the selected engine performancevariables (e.g., altitude, air speed and air temperature) at 60. Asmentioned above, the regression relates the engine performance variablesas a function of engine operating conditions. The metric component 44then determines the residuals of the regression at 62 and applies themetrics (e.g., R-square, mean square error, sum square error and sigmametrics) at 64. The term residuals refers to the differences between theactual values of the dependent variables and their predicted orestimated value for a particular observation. Next, the data cleaningheuristics component 46 cleans the preprocessed data according to a setof heuristics and generates certain statistics such as outliers andleverage points at 66.

The engine baseline modeling component 34 then performs anotherregression at 68. The engine baseline modeling component 34 applies asecond regression to improve the parameter estimates by using a cleanerdata set. Again, the metric component 44 determines additional residualsat 70. Alternatively, the metric component 44 can generate plots of theresiduals if a user desires. At 72, the engine baseline modelingcomponent determines whether there are any more segments that have to beanalyzed. If there are more segments, then the next segment is analyzedat 74 and blocks 60-72 are repeated. This process continues until it isdetermined at 72 that there are no more segments. Once it has beendetermined that there are no more segments, then the model diagnosticscomponent 36 evaluates the performance of the of the engine baselinemodel at 76 and generates certain statistical outputs that relate to themodel.

The foregoing flow chart of this disclosure shows the functionality andoperation of the engine baseline modeling system 28. In this regard,each block represents a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that in somealternative implementations, the functions noted in the blocks may occurout of the order noted in the figures or, for example, may in fact beexecuted substantially concurrently or in the reverse order, dependingupon the functionality involved. Furthermore, the functions can beimplemented in programming languages such as C and Java, however, othercomputer programming languages can be used. Also, the engine servicedatabase 30 may be constructed using commercial databases includingstandard relational, object-oriented or hierarchical databases. Datamanipulation operations, including retrieval, creation, modification,and deletion, can be implemented within the programming languages orwithin the database using stored procedures or triggers or combinations.

The above-described engine baseline modeling system 28 comprises anordered listing of executable instructions for implementing logicalfunctions. The ordered listing can be embodied in any computer-readablemedium for use by or in connection with a computer-based system that canretrieve the instructions and execute them. In the context of thisapplication, the computer-readable medium can be any means that cancontain, store, communicate, propagate, transmit or transport theinstructions. The computer readable medium can be an electronic, amagnetic, an optical, an electromagnetic, or an infrared system,apparatus, or device. An illustrative, but non-exhaustive list ofcomputer-readable mediums can include an electrical connection(electronic) having one or more wires, a portable computer diskette(magnetic), a random access memory (RAM) (magnetic), a read-only memory(ROM) (magnetic), an erasable programmable read-only memory (EPROM orFlash memory) (magnetic), an optical fiber (optical), and a portablecompact disc read-only memory (CDROM) (optical).

Note that the computer readable medium may comprise paper or anothersuitable medium upon which the instructions are printed. For instance,the instructions can be electronically captured via optical scanning ofthe paper or other medium, then compiled, interpreted or otherwiseprocessed in a suitable manner if necessary, and then stored in acomputer memory.

FIG. 4 shows an architectural diagram of a system 78 for implementingthe engine baseline modeling system 28 shown in FIG. 2 in a networkedenvironment. In FIG. 4, a user uses a computing unit 80 to access theengine baseline modeling system 28 and engine service database 30. Morespecifically, the computing unit 80 connects to the engine baselinemodeling system 28 and engine service database 30 through acommunication network 82 such as an electronic or wireless network. Thecomputing unit 80 can take the form of a hand-held digital computer,personal digital assistant computer, notebook computer, personalcomputer or workstation, while the communications network may be aprivate network such as an extranet or intranet or a global network suchas a WAN (e.g., Internet). A web server 84 serves the engine baselinemodeling system 28 and the engine service database 30 to the user in theform of web pages. The web pages can be in the form of HTML, however,other formats and structures can be used such as SGML, XML or XHTML. Theuser uses a web browser 86 running on the computing unit 80 such asMicrosoft INTERNET EXPLORER, Netscape NAVIGATOR or Mosaic to locate anddisplay the web pages generated from the engine baseline modeling system28 and engine service database 30.

If desired, the system 78 may have functionality that enablesauthentication and access control of users accessing the web pageslinked to the engine baseline modeling system 28. Both authenticationand access control can be handled at the web server level by acommercially available package such as Netegrity SITEMINDER. Informationto enable authentication and access control such as the user names,location, telephone number, organization, login identification,password, access privileges to certain resources, physical devices inthe network, services available to physical devices, etc. can beretained in a database directory. The database directory can take theform of a lightweight directory access protocol (LDAP) database,however, other directory type databases with other types of schema maybe used including relational databases, object-oriented databases, flatfiles, or other data management systems.

Referring now to FIG. 5, there is shown a flow diagram illustrating asecond alternative embodiment of the engine baseline modeling component34 as described in blocks 60 through 72 of FIG. 3. In particular, theengine baseline modeling component 34 may be further configured toadjust the output baseline model for time varying effects on themeasured/modeled data and parameters. It should be understood thatvarious ones of the measured types of engine data include time-basedvariations or deteriorations which are expected or normal during theoperation of the engine. That is, as time moves forward, data values maychange to a known degree solely in relation to time, and not due toother external considerations or poor operation. In order to furtherenhance the quality of the baseline model, such time-relateddeterioration effects should be removed from the baseline model.

In the first embodiment described above, an assumption is made regardingthe effect of time on measured trends, in that deterioration timeeffects (i.e., trend variations) over short periods are deemed to bemuch smaller than the measured residuals, thereby rendering their effectminimal. However, if deterioration time effects are not assumed to besmall relative to residuals, less effective models may be generated.Accordingly, the present invention includes a method for reducing theeffect of such deterioration, thereby increasing the usefulness of thegenerated baseline.

In block 88 of FIG. 5, residual values are again calculated as in block70 of FIG. 3, above, creating an initial baseline model. Next, in block90, the initial baseline model is subjected to a smoothing algorithm,such as a moving average, to reduce variations in the identified trends.Once the initial model has been smoothed, the smoothed effect may thenbe eliminated from the initial baseline model in 92, thereby removingits effect on the measured residuals. Essentially, by smoothing theinitial model to remove or reduce trend variations, the remaining modelrepresents only the deterioration time effects on the measuredparameter. Once these effects are isolated, they may be removed from theinitial model through a process known as detrending. Once detrended, anew baseline model may be calculated in block 94. The process thencontinues to block 72 of FIG. 3, where it is determined whetheradditional segments remain to be analyzed.

It should be understood that any suitable method for smoothingvariations on the initial baseline model may be applied and should notbe limited to the moving average methodology described above. In oneembodiment, a Loess algorithm may be employed. Additionally, in anotherembodiment of the present invention, the steps of blocks 90-94 may berepeated to further improve the quality of the baseline model. However,it should be noted that each subsequent iteration of the smoothing anddetrending process results in diminished improvement on model quality.

Referring now to FIG. 6, there is shown a flow diagram illustrating athird alternative embodiment of the engine baseline modeling component34 as described above in blocks 60 through 72 of FIG. 3. In particular,the engine baseline modeling component 34 may be further configured toadjust the output baseline model to include modeling data for variouscombined parameters. Although each measured type of engine data relatesto specific condition or mechanism, correlations between various datatypes can be ascertained. Accordingly, resulting trends from correlateddata types may be combined to reduce the effect of noise in the overallmodel, while preserving the important characteristics of the results.

In block 96 of FIG. 6, residual values are again calculated and all datasegments are analyzed, as determined in block 72 of FIG. 3. At thispoint, correlated segments are identified in block 98. The data fromthese segments is then combined or fused in block 100. In oneembodiment, this data fusion process utilizes a technique known asweighted average.

In this technique, a normalizing transform is performed on each dataset. Once the identified data sets have been normalized, a trend isdetermined for each of the monitored parameters in block. One trend isselected as the primary trend and the remaining trends are fit to theprimary trend. This may be accomplished in any suitable manner, such asregression or data smoothing techniques. By way of explanation, in oneparticular embodiment, three exemplary trends B (blue), G (green), and R(red) have been identified for monitored parameters. Assuming that theblue (B) trend has been established as the primary trend, the R and Gtrends are then fit to the primary B trend. This may be done by fittingthe models, B[i]=a1*R[i]+b1 and B[i]=a2*G[i]+b2. New R and G datasetsare then generated from R*[i]=a1*R[i]+b1 and G*[i]=a2*G[i]+b2.

If the models fit well, then BLUE[i], RED*[i], and GREEN*[i] should allbe approximately the same value, where Red and Green have beennormalized to fit the primary Blue trend. In this manner, 3 times asmany observations are utilized in a single dataset than would have beenutilized by just using the BLUE series alone. Accordingly, the finalmodel is then based upon more information.

Of course, since the data isn't always as nice as the example,circumstances exist wherein different trend data is not combinedtogether in this manner. For example, fluctuations in one trend thatdoesn't occur in the other two may be ignored. Additionally, thesmoothing concept briefly described above comes into play when the R, G,B trends are less than perfect, and may include significant amounts ofnoise. By smoothing, with a moving average for example, smooth trendsR′, G′, B′, may be identified. In one embodiment, B′[i]=a1*R′[i]+b1 andB′[i]=a2*G′[i]+b2, may be utilized to generate a smoothed B′ trend. NewR and G datasets are then generated as before. This technique isparticularly useful when measuring the same quantity, such as fuel flowor exhaust gas temperature, at different operational points, such asaircraft cruise (steady state), takeoff (maximum transient), and climb(maximum sustained).

Next, once normalized, data sets are combined to form a single data set,effectively representing a new engine parameter relating to engineperformance. Once the data has been combined, a regression on thecombined data is performed in 102 and the residuals are determined inblock 104, resulting in a baseline model for the combined parameters,which may be a better indicator of engine performance, thereby enablingmore rapid and accurate monitoring and diagnosis. The process thencontinues to model diagnostics generation block 76, described in FIG. 3.

Referring now to FIG. 7, there is shown a flow diagram illustrating afourth alternative embodiment of the engine baseline modeling component34 as described above in blocks 60 through 72 of FIG. 3. In particular,the engine baseline modeling component 34 may be further configured todevelop baseline models for pairs or other groupings of engines. Becauseengines are rarely used alone, it may be desirable to develop baselinemodels for pairs of engines which will be used together, since pairedsystems operate under the same environment. Further, because the averageoutput of engine pairs is tied to their average input, it is determinedthat the model for the entire system may be more accurate if operationalconditions and outputs of the paired system as a whole are considered.

Initially, in block 106, once all data segments have been initiallyanalyzed in step 72 of FIG. 3, information from related pairs of enginesare identified. Next, the data calculated for each paired value isaveraged in step 108. In step 110, the averaged data is included withinanother regression analysis to further reduce estimation error in thediagnosis, thereby resulting in a more accurate baseline model. One formof doing this is to simply modify model equation (1) recited above from:Y=f(altitude, temperature, power setting, air speed)  (1),toY=f(altitude, temperature, power setting, air speed, power setting otherengine),  (4)

since the other 3 elements remain constant for both engines. A morecomplex manner of performing this regression is to also add the previousY result from the other engine:Y=f(atitude, temperature, power setting, air speed, power setting otherengine, Y other engine)  (5)

In this manner, the present methodology man be extended to 3 and 4engine aircraft as well as the above described two engine embodiment.

It is apparent that there has been provided in accordance with thisinvention, a baseline modeling system, method and computer product.While the foregoing description includes many details and specificities,it is to be understood that these have been included for purposes ofexplanation only, and are not to be interpreted as limitations of thepresent invention. Many modifications to the embodiments described abovecan be made without departing from the spirit and scope of theinvention, as is intended to be encompassed by the following claims andtheir legal equivalents.

1. A system for performing engine baseline modeling, comprising: an engine service database containing engine data, wherein the engine data includes at least time-varying engine data; a preprocessor for processing the engine data into a predetermined format; an engine baseline modeling component that builds an initial engine baseline model from the preprocessed data using a regression analysis, wherein the regression analysis relates engine performance variables as a function of engine operating conditions, wherein the engine baseline modeling component: applies a smoothing algorithm to the initial engine baseline model to generate a smoothed effect, eliminates the smoothed effect from the initial engine baseline model to isolate a plurality of deterioration time effects on a measured parameter, and removes the deterioration time effects from the initial engine baseline model to generate a detrended engine baseline model; and a model diagnostic component that evaluates the performance of the detrended engine baseline model.
 2. The system of claim 1, wherein the smoothing algorithm includes a moving average calculation.
 3. The system of claim 1, further comprising the engine baseline modeling component performing repeated applications of the smoothing algorithm to the detrended engine baseline model.
 4. The system of claim 1, wherein the preprocessor comprises a data acquisition component that extracts the engine data from the engine services database.
 5. The system of claim 1, wherein the preprocessor comprises a data scrubbing component that cleans the engine data.
 6. The system of claim 1, wherein the preprocessor comprises a data segmenting component that segments the engine data into a plurality of groups.
 7. The system of claim 1, wherein the engine baseline modeling component comprises a metric component that validates the detrended engine baseline model.
 8. The system of claim 1, wherein the engine baseline modeling component comprises a heuristics component that generates rules for cleaning the preprocessed data.
 9. A system for performing engine baseline modeling, comprising: an engine service database containing engine data; a preprocessor for processing the engine data into a predetermined format, wherein the preprocessor comprises a data segmenting component that segments the engine data into a plurality of groups; an engine baseline modeling component that builds an initial engine baseline model from the preprocessed data using a regression analysis, wherein the regression analysis relates engine performance variables as a function of engine operating conditions, wherein the engine baseline modeling component identifies correlated groups of engine data based upon the initial engine baseline model, wherein the engine baseline modeling component combines data from correlated groups, and wherein the engine baseline modeling component builds a final engine baseline model from the combined data using a regression analysis; and a model diagnostic component that evaluates the performance of the final engine baseline model.
 10. The system of claim 9, wherein the combination of data from correlated groups is performed by utilizing a weighted average technique to fit all engine baseline parameter trends to one primary trend.
 11. A system for performing engine baseline modeling, comprising: an engine service database containing engine data; a preprocessor for processing the engine data into a predetermined format, wherein the preprocessor comprises a data segmenting component that segments the engine data into a plurality of groups; an engine baseline modeling component that builds an initial engine baseline model from the preprocessed data using a regression analysis, the initial engine baseline model represented by a plurality of parameter estimates, wherein the regression analysis relates engine performance variables as a function of engine operating conditions, wherein the engine baseline modeling component identifies segments relating to related engines, wherein the engine baseline modeling component smoothes the parameter estimates for each of the identified related engine segments, and wherein the engine baseline modeling component builds a final engine baseline model from the averaged data using a regression analysis; and a model diagnostic component that evaluates the performance of the final engine baseline model.
 12. A method for performing engine baseline modeling, comprising: storing engine data in an engine service database, wherein the engine data includes at least time-varying engine data; processing the engine data into a predetermined format; building an initial engine baseline model from the processed data using a regression analysis, wherein the regression analysis relates engine performance variables as a function of engine operating conditions, applying a smoothing algorithm to the initial engine baseline model to generate a smoothed effect; eliminating the smoothed effect from the initial engine baseline model to isolate a plurality of deterioration time effects on a measured parameter; and removing the deterioration time effects from the initial engine baseline model to generate a detrended engine baseline model; and using the detrended baseline model to perform at least one of monitoring engine status, predicting future engine behavior, diagnosing engine faults, determining engine performance, determining engine quality and designing new engine systems.
 13. The method of claim 12, wherein the smoothing algorithm includes a moving average calculation.
 14. The method of claim 12, further comprising repeatedly applying the smoothing algorithm to the detrended engine baseline model.
 15. The method of claim 12, further comprising extracting the engine data from the engine services database.
 16. The method of claim 12, wherein the processing step further comprises cleaning the engine data.
 17. The method of claim 12, wherein the processing step further comprises segmenting the engine data into a plurality of groups.
 18. The method of claim 12, further comprising validating the detrended engine baseline model.
 19. The method of claim 12, further comprising generating rules for cleaning the preprocessed data.
 20. The method of claim 12, further comprising evaluating the performance of the detrended engine baseline model.
 21. A method for performing engine baseline modeling, comprising: storing engine data in an engine service database; processing the engine data into a predetermined format; segmenting the engine data into a plurality of groups; building an initial engine baseline model from the processed data using a regression analysis, wherein the regression analysis relates engine performance variables as a function of engine operating conditions; identifying correlated groups of engine data based upon the initial engine baseline model; combining data from correlated groups; building a final engine baseline model from the combined data using a regression analysis; and using the final engine baseline model to perform at least one of monitoring engine status, predicting future engine behavior, diagnosing engine faults, determining engine performance, determining engine quality and designing new engine systems.
 22. The method of claim 21, wherein the step of combining of data from correlated groups comprises utilizing a weighted average technique to fit all engine baseline parameter trends to one primary trend.
 23. A method for performing engine baseline modeling, comprising: storing engine data in an engine service database; processing the engine data into a predetermined format; segmenting the engine data into a plurality of groups; building an initial engine baseline model from the processed data using a regression analysis, the initial engine baseline model represented by a plurality of parameter estimates, wherein the regression analysis relates engine performance variables as a function of engine operating conditions; identifying segments relating to related engines; smoothing the parameter estimates for each of the identified related engine segments; building a final engine baseline model from the averaged data using a regression analysis; and using the final engine baseline model to perform at least one of monitoring engine status, predicting future engine behavior, diagnosing engine faults, determining engine performance, determining engine quality and designing new engine systems.
 24. A computer-readable medium storing computer instructions for instructing a computer system to perform engine baseline modeling, the computer instructions comprising: one or more instructions for storing engine data in an engine service database, wherein the engine data includes at least time-varying engine data; one or more instructions for processing the engine data into a predetermined format; one or more instructions for building an initial engine baseline model from the processed data using a regression analysis, wherein the regression analysis relates engine performance variables as a function of engine operating conditions; one or more instructions for applying a smoothing algorithm to the initial engine baseline model to generate a smoothed effect; one or more instructions for eliminating the smoothed effect from the initial engine baseline model to isolate a plurality of deterioration time effects on a measured parameter; and one or more instructions for removing the deterioration time effects from the initial engine baseline model to generate a detrended engine baseline model; and one or more instructions for using the detrended baseline model to perform at least one of monitoring engine status, predicting future engine behavior, diagnosing engine faults, determining engine performance, determining engine quality and designing new engine systems.
 25. The computer-readable medium of claim 24, wherein the smoothing algorithm includes a moving average calculation.
 26. The computer-readable medium of claim 24, further comprising one or more instructions for repeatedly applying the smoothing algorithm to the detrended engine baseline model.
 27. The computer-readable medium of claim 24, further comprising one or more instructions for extracting the engine data from the engine services database.
 28. The computer-readable medium of claim 24, wherein the one or more instructions for processing further comprise one or more instructions for cleaning the engine data.
 29. The computer-readable medium of claim 24, wherein the one or more instructions for processing further comprise one or more instructions for segmenting the engine data into a plurality of groups.
 30. The computer-readable medium of claim 24, further comprising one or more instructions for validating the detrended engine baseline model.
 31. The computer-readable medium of claim 24, further comprising one or more instructions for generating rules for cleaning the preprocessed data.
 32. The computer-readable medium of claim 24, further comprising one or more instructions for evaluating the performance of the detrended engine baseline model.
 33. A computer-readable medium storing computer instructions for instructing a computer system to perform engine baseline modeling, the computer instructions comprising: one or more instructions for storing engine data in an engine service database; one or more instructions for processing the engine data into a predetermined format; one or more instructions for segmenting the engine data into a plurality of groups; one or more instructions for building an initial engine baseline model from the processed data using a regression analysis, wherein the regression analysis relates engine performance variables as a function of engine operating conditions; one or more instructions for identifying correlated groups of engine data based upon the initial engine baseline model; one or more instructions for combining data from correlated groups; one or more instructions for building a final engine baseline model from the combined data using a regression analysis, and one or more instructions for using the final engine baseline model to perform at least one of monitoring engine status, predicting future engine behavior, diagnosing engine faults, determining engine performance, determining engine quality and designing new engine systems.
 34. The computer-readable medium of claim 21, wherein the one or more instructions for combining data from correlated groups comprises one or more instructions for applying a weighted average technique to fit all engine baseline parameter trends to one primary trend.
 35. A computer-readable medium storing computer instructions for instructing a computer system to perform engine baseline modeling, the computer instructions comprising: one or more instructions for storing engine data in an engine service database; one or more instructions for processing the engine data into a predetermined format; one or more instructions for segmenting the engine data into a plurality of groups; one or more instructions for building an initial engine baseline model from the processed data using a regression analysis, the initial engine baseline model represented by a plurality of parameter estimates, wherein the regression analysis relates engine performance variables as a function of engine operating conditions; one or more instructions for identifying segments relating to related engines; one or more instructions for smoothing the parameter estimates for each of the identified related engine segments; one or more instructions for building a final engine baseline model from the averaged data using a regression analysis, and one or more instructions for using the final engine baseline model to perform at least one of monitoring engine status, predicting future engine behavior, diagnosing engine faults, determining engine performance, determining engine quality and designing new engine systems. 